Overview

Dataset statistics

Number of variables12
Number of observations2966
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.2 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtd_invoices and 3 other fieldsHigh correlation
recency is highly overall correlated with qtd_invoicesHigh correlation
qtd_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtd_items is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtd_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qtd_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.41723734)Skewed
returns is highly skewed (γ1 = 51.77229749)Skewed
avg_basket_size is highly skewed (γ1 = 44.6542745)Skewed
customer_id has unique valuesUnique
recency has 34 (1.1%) zerosZeros
returns has 1480 (49.9%) zerosZeros

Reproduction

Analysis started2023-11-08 11:17:28.397245
Analysis finished2023-11-08 11:18:39.036018
Duration1 minute and 10.64 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.646
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:39.560294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.25
Q113799.75
median15220.5
Q316769.5
95-th percentile17964.75
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.3683
Coefficient of variation (CV)0.11259302
Kurtosis-1.2062852
Mean15270.646
Median Absolute Deviation (MAD)1487
Skewness0.031911548
Sum45292737
Variance2956227.2
MonotonicityNot monotonic
2023-11-08T08:18:40.204711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2956) 2956
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2951
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2751.6337
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:40.871473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile230.9525
Q1571.02
median1088.53
Q32310.295
95-th percentile7226.025
Maximum279138.02
Range279131.82
Interquartile range (IQR)1739.275

Descriptive statistics

Standard deviation10585.697
Coefficient of variation (CV)3.847059
Kurtosis353.6074
Mean2751.6337
Median Absolute Deviation (MAD)673.06
Skewness16.769761
Sum8161345.4
Variance1.1205698 × 108
MonotonicityNot monotonic
2023-11-08T08:18:41.607507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2053.02 2
 
0.1%
331 2
 
0.1%
734.94 2
 
0.1%
1025.44 2
 
0.1%
598.2 2
 
0.1%
533.33 2
 
0.1%
731.9 2
 
0.1%
2092.32 2
 
0.1%
379.65 2
 
0.1%
745.06 2
 
0.1%
Other values (2941) 2946
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
70.02 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.192852
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:42.369976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.574664
Coefficient of variation (CV)1.2084627
Kurtosis2.7616799
Mean64.192852
Median Absolute Deviation (MAD)26
Skewness1.7950064
Sum190396
Variance6017.8286
MonotonicityNot monotonic
2023-11-08T08:18:43.052483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
3 85
 
2.9%
2 84
 
2.8%
8 76
 
2.6%
10 67
 
2.3%
7 66
 
2.2%
9 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2217
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 84
2.8%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 3
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtd_invoices
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7242077
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:43.705403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8551469
Coefficient of variation (CV)1.5469646
Kurtosis191.00754
Mean5.7242077
Median Absolute Deviation (MAD)2
Skewness10.770512
Sum16978
Variance78.413626
MonotonicityNot monotonic
2023-11-08T08:18:44.310867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.5%
3 497
16.8%
4 394
13.3%
5 236
 
8.0%
1 189
 
6.4%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 68
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 189
 
6.4%
2 785
26.5%
3 497
16.8%
4 394
13.3%
5 236
 
8.0%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 68
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%

qtd_items
Real number (ℝ)

HIGH CORRELATION 

Distinct1672
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1610.2599
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:44.919701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile103
Q1297
median642
Q31401
95-th percentile4408
Maximum196844
Range196842
Interquartile range (IQR)1104

Descriptive statistics

Standard deviation5890.3815
Coefficient of variation (CV)3.6580315
Kurtosis465.56335
Mean1610.2599
Median Absolute Deviation (MAD)422.5
Skewness17.850458
Sum4776031
Variance34696595
MonotonicityNot monotonic
2023-11-08T08:18:45.592616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
288 8
 
0.3%
246 8
 
0.3%
272 8
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
114 7
 
0.2%
493 7
 
0.2%
Other values (1662) 2883
97.2%
ValueCountFrequency (%)
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%

qtd_products
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.81187
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:46.260811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation270.00815
Coefficient of variation (CV)2.1985509
Kurtosis354.60552
Mean122.81187
Median Absolute Deviation (MAD)44
Skewness15.702618
Sum364260
Variance72904.399
MonotonicityNot monotonic
2023-11-08T08:18:46.899311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
29 35
 
1.2%
35 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
25 31
 
1.0%
27 30
 
1.0%
26 30
 
1.0%
Other values (457) 2626
88.5%
ValueCountFrequency (%)
1 5
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 28
0.9%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2697 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1672 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2964
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.936044
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:47.523422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9143418
Q113.119823
median17.977335
Q324.991714
95-th percentile90.50125
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.871892

Descriptive statistics

Standard deviation1037.4582
Coefficient of variation (CV)19.975687
Kurtosis2887.7878
Mean51.936044
Median Absolute Deviation (MAD)5.9963256
Skewness53.417237
Sum154042.31
Variance1076319.5
MonotonicityNot monotonic
2023-11-08T08:18:48.213808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.47833333 2
 
0.1%
4.162 2
 
0.1%
18.15222222 1
 
< 0.1%
12.949 1
 
< 0.1%
16.29372093 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2954) 2954
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION 

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.378936
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:49.590707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median48.392857
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.333333

Descriptive statistics

Standard deviation63.561155
Coefficient of variation (CV)0.94333867
Kurtosis4.8826514
Mean67.378936
Median Absolute Deviation (MAD)26.27381
Skewness2.06229
Sum199845.92
Variance4040.0205
MonotonicityNot monotonic
2023-11-08T08:18:50.387767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
70 21
 
0.7%
4 21
 
0.7%
7 20
 
0.7%
35 18
 
0.6%
49 18
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
11 17
 
0.6%
28 16
 
0.5%
Other values (1248) 2776
93.6%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 21
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06316847
Minimum0.0054495913
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:51.020894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0094339623
Q10.017777778
median0.029296366
Q30.055319294
95-th percentile0.22222222
Maximum3
Range2.9945504
Interquartile range (IQR)0.037541516

Descriptive statistics

Standard deviation0.13440508
Coefficient of variation (CV)2.1277241
Kurtosis121.98493
Mean0.06316847
Median Absolute Deviation (MAD)0.014270062
Skewness8.7943007
Sum187.35768
Variance0.018064725
MonotonicityNot monotonic
2023-11-08T08:18:51.639493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333333333 21
 
0.7%
0.1666666667 21
 
0.7%
0.02777777778 20
 
0.7%
0.09090909091 19
 
0.6%
0.0625 17
 
0.6%
0.1333333333 16
 
0.5%
0.25 15
 
0.5%
0.02380952381 15
 
0.5%
0.4 15
 
0.5%
0.03571428571 15
 
0.5%
Other values (1339) 2792
94.1%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005509641873 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
2 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.5 3
 
0.1%
1 14
0.5%
0.8333333333 1
 
< 0.1%
0.75 1
 
< 0.1%
0.6666666667 12
0.4%
0.6514745308 1
 
< 0.1%
0.6 1
 
< 0.1%

returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.124073
Minimum0
Maximum80995
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:52.256305image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1513.2549
Coefficient of variation (CV)24.358591
Kurtosis2762.7859
Mean62.124073
Median Absolute Deviation (MAD)1
Skewness51.772297
Sum184260
Variance2289940.4
MonotonicityNot monotonic
2023-11-08T08:18:52.930618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 147
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
7 43
 
1.4%
8 43
 
1.4%
Other values (204) 705
23.8%
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 147
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.04185
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:53.594094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.291667
Q1103.30833
median172.38095
Q3281.92308
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.61474

Descriptive statistics

Standard deviation791.92626
Coefficient of variation (CV)3.1671748
Kurtosis2253.5421
Mean250.04185
Median Absolute Deviation (MAD)83.083333
Skewness44.654274
Sum741624.13
Variance627147.2
MonotonicityNot monotonic
2023-11-08T08:18:54.286950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
136 8
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
197 7
 
0.2%
Other values (1969) 2879
97.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct1006
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.174744
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-08T08:18:54.953497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4802632
Q110.020833
median17.2
Q327.75
95-th percentile57
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.729167

Descriptive statistics

Standard deviation19.523034
Coefficient of variation (CV)0.88041754
Kurtosis27.655868
Mean22.174744
Median Absolute Deviation (MAD)8.2
Skewness3.4969867
Sum65770.292
Variance381.14885
MonotonicityNot monotonic
2023-11-08T08:18:55.597652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 39
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 31
 
1.0%
17 31
 
1.0%
18 30
 
1.0%
10 30
 
1.0%
16 29
 
1.0%
Other values (996) 2619
88.3%
ValueCountFrequency (%)
1 31
1.0%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

Interactions

2023-11-08T08:18:31.711439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:29.574208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:35.391562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:40.686205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:46.185894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:51.422513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:57.628976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:03.485657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:09.022353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:14.958961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:20.550022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:26.155241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:32.145696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:30.006286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:35.787535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:41.148243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:46.663805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:51.853409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:58.141814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:03.977525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:09.443002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:15.379909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:20.980504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:26.583293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:32.583386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:30.491919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:36.172355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:41.566510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:47.050564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:52.890552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:58.676618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:04.382465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:09.861485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:15.802467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:21.405340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:26.999359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:33.020959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:30.932694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:36.594160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:42.066962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:47.474341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:53.353593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:59.172670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:04.828598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:10.282005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:16.337734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:21.855258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:27.461195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:34.112890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:31.758447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:36.993478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:42.458960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:47.922970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:53.844170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:59.627604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:05.232144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:10.663147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:16.761417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:22.337965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:27.861262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:34.600845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:32.275250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:37.450855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:42.950835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:48.353841image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:54.344435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:00.106396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:05.762683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:11.167438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:17.216407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:22.831378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:28.332912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:35.032320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:32.814383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:37.880657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:43.431434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:48.826370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:54.856460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:00.596072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:06.270156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:11.612122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:17.664462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:23.331346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:28.804223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:35.453560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:33.205032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:38.319449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:43.868473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:49.215803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:55.266186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:01.026877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:06.749695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:12.025538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:18.132909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:23.824349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:29.248659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:35.874989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:33.631011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:38.749901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:44.335523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:49.716822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:55.716729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:01.510312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:07.173672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:12.503965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:18.609993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:24.311691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:29.776578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:36.333923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:34.054226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:39.176295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:44.758117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:50.133443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:56.176505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:01.945900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:07.590410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:13.581445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:19.067768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:24.812610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:30.249798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:36.768528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:34.491171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:39.706237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:45.211502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:50.565614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:56.676258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:02.494029image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:08.063379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:14.059888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:19.591550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:25.275803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:30.769598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:37.228073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:34.935332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:40.192622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:45.688799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:51.019266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:17:57.138643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:02.998045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:08.593969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:14.543863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:20.135248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:25.722339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-08T08:18:31.252970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-08T08:18:56.044381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.077-0.0000.026-0.0710.012-0.1300.018-0.008-0.064-0.124-0.008
gross_revenue-0.0771.000-0.4150.7700.9250.7430.246-0.2490.1620.3730.5740.289
recency-0.000-0.4151.000-0.502-0.407-0.4350.0480.108-0.032-0.121-0.097-0.106
qtd_invoices0.0260.770-0.5021.0000.7160.6900.059-0.2600.1500.2950.0990.024
qtd_items-0.0710.925-0.4070.7161.0000.7290.167-0.2290.1470.3450.7290.319
qtd_products0.0120.743-0.4350.6900.7291.000-0.378-0.1670.1020.2430.3820.699
avg_ticket-0.1300.2460.0480.0590.167-0.3781.000-0.1220.0990.1900.187-0.612
avg_recency_days0.018-0.2490.108-0.260-0.229-0.167-0.1221.000-0.962-0.397-0.0790.047
frequency-0.0080.162-0.0320.1500.1470.1020.099-0.9621.0000.3600.058-0.041
returns-0.0640.373-0.1210.2950.3450.2430.190-0.3970.3601.0000.2100.019
avg_basket_size-0.1240.574-0.0970.0990.7290.3820.187-0.0790.0580.2101.0000.446
avg_unique_basket_size-0.0080.289-0.1060.0240.3190.699-0.6120.047-0.0410.0190.4461.000

Missing values

2023-11-08T08:18:37.930338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-08T08:18:38.712686image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.01733.0297.018.15222235.5000000.48611140.050.9705888.735294
1130473232.5956.09.01390.0171.018.90403527.2500000.04878035.0154.44444419.000000
2125836705.382.015.05028.0232.028.90250023.1875000.04569950.0335.20000015.466667
313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000005.600000
415100876.00333.03.080.03.0292.0000008.6000000.13636422.026.6666671.000000
5152914623.3025.014.02102.0102.045.32647123.2000000.05444129.0150.1428577.285714
6146885630.877.021.03621.0327.017.21978618.3000000.073569399.0172.42857115.571429
7178095411.9116.012.02057.061.088.71983635.7000000.03910641.0171.4166675.083333
81531160767.900.091.038194.02379.025.5434644.1444440.315508474.0419.71428626.142857
9160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714299.571429
customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
5602177271060.2515.01.0645.066.016.0643946.00.2857146.0645.00000066.0
561217232421.522.02.0203.036.011.70888912.00.1538460.0101.50000018.0
561317468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.5
562413596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000083.0
5630148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.5
563412479473.2011.01.0382.030.015.7733334.00.33333334.0382.00000030.0
565514126706.137.03.0508.015.047.0753333.01.00000050.0169.3333335.0
5661135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333145.0
567115060301.848.04.0262.0120.02.5153331.02.0000000.065.50000030.0
569012558269.967.01.0196.011.024.5418186.00.285714196.0196.00000011.0